Collaborative Research: MSA: Incorporating Canopy Structural Complexity to Improve Model Forecasts of Functional Effects of Forest Disturbance

火灾、风暴和虫害等扰动会改变森林的物理结构，影响森林的生长速度。严重的干扰往往降低森林的生长率；这些事件研究得很充分，但很少见。大多数干扰比较温和，可以通过重塑森林冠层来增加光捕获和碳吸收，从而提高生长速度。生态系统模型往往不能预测森林对中度干扰的反应。部分原因是他们依赖于简单的森林结构概念。遥感方法可以探测干扰和测量对森林结构的影响。该项目将这些方法结合起来，以改进森林对中度干扰响应的模型预测。更好的模型预测将有助于改善森林管理实践。这项研究将利用几十年的陆地卫星图像来绘制最近（5年前）在森林中的氖点的扰动。NEON空中观测平台（AOP）的激光雷达将通过计算与生态系统功能相关的几个结构指标来表征这些地点的树冠结构。我们将通过比较受干扰地区和附近未受干扰地区的冠层结构来识别几种森林扰动的结构特征。干扰类型和严重程度的范围，结合森林类型的广泛多样性，将提供对森林结构干扰后果的宏观系统层面的理解。我们将使用干扰和冠层结构的结果图来测试生态系统人口学（ED2）模型在捕捉干扰的结构和功能结果方面的技能。通过模型实验，我们将通过将NEON AOP结构数据吸收到ED2中来量化净初级生产和净生态系统生产的改进。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Disturbances such as fire, storms, and insect outbreaks change the physical structure of forests and affect growth rates. Severe disturbances often reduce forest growth rates; these events are well-studied but rare. Most disturbances are more moderate and can boost growth rates by reshaping forest canopies to increase light capture and carbon uptake. Ecosystem models often fail to predict forest responses to moderate disturbances. This is partly because they rely on simplistic concepts of forest structure. Remote sensing methods can detect disturbances and measure impacts on forest structure. This project combines these methods to improve model predictions of forest responses to moderate disturbance. Better model predictions will help improve forest management practices. The research will leverage decades of Landsat satellite imagery to map recent (5 years old) disturbances at forested NEON sites. LiDAR from the NEON Aerial Observation Platform (AOP) will characterize canopy structure at these sites by calculating several structural metrics with demonstrated links to ecosystem functions. We will identify structural signatures of several forest disturbances by comparing canopy structure in disturbed areas to nearby undisturbed areas. The range of disturbance types and severity, in combination with a wide diversity of forest types will provide a macrosystem-level understanding of disturbance consequences for forest structure. We will use the resulting maps of disturbance and canopy structure to test the skill of the Ecosystem Demography (ED2) model at capturing the structural and functional outcomes of disturbance. Using model experiments, we will quantify the improvements in Net Primary Production and Net Ecosystem Production by assimilating NEON AOP structural data into ED2.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.